Method for real-time scheduling of multi-energy complementary micro-grids based on rollout algorithm

ABSTRACT

The invention relates to a method for real-time scheduling of multi-energy complementary micro-grids based on a Rollout algorithm, which is technically characterized by comprising the following steps of: Step 1, setting up a moving-horizon Markov decision process model for the real-time scheduling of the multi-energy complementary micro-grids with random new-energy outputs, and establishing constraint conditions for the real-time scheduling; Step 2, establishing a target function of the real-time scheduling; Step 3, dividing a single complete scheduling cycle into a plurality of scheduling intervals, and finding one basic feasible solution meeting the constraint conditions for the real-time scheduling based on a greedy algorithm; and Step 4, finding a solution to the moving-horizon Markov decision process model for the real-time scheduling of the multi-energy complementary micro-grids by using the Rollout algorithm based on the basic feasible solution from Step 3. With the consideration of the fluctuations in the new-energy outputs, the present invention solves the problems of low speed and low efficiency of a traditional algorithm at the same time, enabling high-speed efficient multi-energy complementary micro-grid real-time scheduling.

TECHNICAL FIELD

The invention generally belongs to the technical field of multi-energycomplementary micro-grids, and relates to a method for real-timescheduling of multi-energy complementary micro-grids, and in particularto a method for real-time scheduling of multi-energy complementarymicro-grids based on a Rollout algorithm

BACKGROUND

As the smart grid technology evolves, a multi-energy complementarymicro-grid system incorporating new energies with an energy storagefeature has aroused widespread concerns from researchers. As anautonomous system capable of self-control, protection and management,the multi-energy complementary micro-grids can facilitate theutilization of distributed energy on the spot and enable highly reliablesupply of various forms of energy loaded in a more economic and friendlyway, transitioning from the traditional grid to the smart grid.

The fluctuation and intermittence in the new-energy outputs pose greatchallenges to the real-time scheduling of the multi-energy complementarymicro-grids, and since the real-time scheduling is a moving-horizonprocess, the control behavior in a current scheduling interval not onlyaffects the current cycle but also affects the state of a nextscheduling interval. The Markov decision model provides a good idea tosolve this moving scheduling problem with uncertain variables, but thelarge number of variables brings a disaster on dimension, leading to adifficulty in finding a solution to the model, and how to find aneffective method to solve the difficulty above has become the key toreal-time scheduling.

SUMMARY OF THE INVENTION

An objective of the present invention is to overcome the deficiencies ofthe prior art, and to provide a method for real-time scheduling ofmulti-energy complementary micro-grids based on a Rollout algorithm,which is simple, feasible, efficient and rapid with reasonable designand high practicability.

The invention solves the technical problem with the following technicalsolution:

-   a method for real-time scheduling of multi-energy complementary    micro-grids based on a Rollout algorithm, characterized by    comprising the following steps of:-   Step 1, setting up a moving-horizon Markov decision process model    for the real-time scheduling of the multi-energy complementary    micro-grids with random new-energy outputs, and establishing    constraint conditions for the real-time scheduling;-   Step 2, establishing a target function of the real-time scheduling    for the moving-horizon Markov decision process model for the    real-time scheduling of the multi-energy complementary micro-grids    with random new-energy outputs, with the goal of minimum operating    cost of a micro-grid system in a moving-horizon Markov decision    cycle;-   Step 3, dividing a single complete scheduling cycle into a plurality    of scheduling intervals, and finding one basic feasible solution    meeting the constraint conditions for the real-time scheduling based    on a greedy algorithm; and

Step 4, finding a solution to the moving-horizon Markov decision processmodel for the real-time scheduling of the multi-energy complementarymicro-grids by using the Rollout algorithm based on the basic feasiblesolution from Step 3.

Furthermore, the constraint conditions established for the real-timescheduling in Step 1 comprise: micro-grid electric equilibriumconstraints, storage battery operating constraints, exchange electricpower constraints for the micro-grids and a main grid, and electricpower output constraints for combined heat and power equipment;

the micro-grid electric equilibrium constraints are as follows:

${{p^{G}(t)} + {\sum\limits_{i = 1}^{N}{p_{i}^{c}(t)}} + {p^{B}(t)} + {p^{w}(t)}} = {p^{D}(t)}$

in the formula, t is a time parameter; p^(G) (t) is exchange electricpower for the micro-grids and the main grid at a time t, which ispositive during the purchasing of electricity from the main grid andnegative during selling of electricity to the main grid; N is thequantity of the combined heat and power equipment; p_(i) ^(c)(t) is theoutput electric power of the ith combined heat and power equipment atthe time t; p^(B)(t) is charging/discharging power of the storagebattery at the time t, which is negative during charging and positiveduring discharging; p^(w)(t) is generated output of wind power at thetime t; and p^(D)(t) is an electric load demand at the time t;the storage battery operating constraints are as follows:

$\quad\left\{ \begin{matrix}{{E\left( {t + 1} \right)} = {{E(t)} = {{{p^{B}(t)} \cdot \Delta}\; {T \cdot \alpha_{c}}}}} \\{{E\left( {t + 1} \right)} = {{E(t)} - {{{p^{B}(t)} \cdot \Delta}\; T\text{/}\alpha_{d}}}} \\{\underset{\_}{E} \leq {E(t)} \leq \overset{\_}{E}} \\{{{p^{B}(t)}} \leq \overset{\_}{p^{B}}}\end{matrix} \right.$

in the formulae, E(t) and E(t+1) are energy storage levels of thestorage battery at the time t and a time t+1 respectively; E and Ē areupper and lower boundaries of the capacity of the storage batteryrespectively; ΔT is a time interval from the time t to the time t+1;a_(c) and a_(d) are charging and discharging efficiencies of the storagebattery respectively; p^(B) (t) is charging/discharging power of thestorage battery at the time t, which is negative during charging andpositive during discharging; and p^(B) is an upper limit value of thecharging/discharging power of the storage battery;the exchange electric power constraints for the micro-grids and the maingrid are as follows:

$\quad\left\{ \begin{matrix}{{{p^{G}(t)}} \leq \overset{\_}{p^{G}}} \\{{{{p^{G}(t)} - {p^{G}\left( {t - 1} \right)}}} \leq \delta}\end{matrix} \right.$

in the formulae, p^(G)(t) and p^(G)(t−1) are exchange electric powerbetween the micro-grids and the main grid at the times t and t−1respectively, which is positive during purchasing of electricity fromthe main grid and negative during selling of electricity to the maingrid; p^(G) is an upper limit value of the exchange electric powerbetween the micro-grids and the main grid, and δ is an upper fluctuationlimit of the exchange electric power between the micro-grids and themain grid;the electric power output constraints for the combined heat and powerequipment are as follows:

$\quad\left\{ \begin{matrix}{{0 \leq {p_{i}^{c}(t)} \leq \overset{\_}{p_{i}^{c}}},{i = 1},2,\ldots \mspace{14mu},N} \\{{\underset{\_}{v_{i}} \cdot {H_{i}(t)}} \leq {p_{i}^{c}(t)} \leq {\overset{\_}{v_{i}} \cdot {H_{i}(t)}}}\end{matrix} \right.$

in the formulae, p_(i) ^(c)(t) is output electric power of the ithcombined heat and power equipment at the time t; p_(i) ^(c) is an upperlimit of the output electric power of the ith combined heat and powerequipment; H_(i)(t) is thermal power required to be supplied by the ithcombined heat and power equipment at the time t; v_(i) and v_(i) arelower and upper limits of electric-thermal power conversion efficiencyof the ith combined heat and power equipment.

Furthermore, establishing a target function of the real-time schedulingfor the moving-horizon Markov decision process model for the real-timescheduling of the multi-energy complementary micro-grids with randomnew-energy outputs in Step 2 specifically comprises the followingsub-steps of: first setting up an operating cost function of themicro-grid system at a single scheduling interval with the goal ofminimum operating cost of the micro-grid system at the single schedulinginterval, and then establishing a target function of the real-timescheduling with the goal of the minimum operating cost of the micro-gridsystem in the moving-horizon Markov decision cycle;

the operating cost function of the micro-grid system at the singlescheduling interval is as follows:

$\begin{matrix}{{c_{t}\left( {{X(t)},{A(t)}} \right)} = {{{{\lambda (t)} \cdot {p^{G}(t)} \cdot \Delta}\; T} + {c \cdot {\sum\limits_{i = 1}^{N}{F_{i}^{c}(t)}}}}} & \; \\{{wherein}\mspace{695mu}} & \; \\\left\{ \begin{matrix}{{F_{i}^{c}(t)} = {{a_{i} \cdot {p_{i}^{c}(t)}} + b_{i}}} \\{{X(t)} = \left\lbrack {{E(t)},{p^{G}\left( {t - 1} \right)},{p^{w}(t)}} \right\rbrack} \\{{A(t)} = \left\lbrack {{p_{i}^{c}(t)},{p^{G}(t)},{p^{B}(t)}} \right\rbrack}\end{matrix} \right. & \;\end{matrix}$

in the formulae, X(t) is a state variable of the micro-grid system atthe time t; A(t) is a control variable of the micro-grid system at thetime t; c_(t)(X(t),A(t)) is a function of the system operating cost atthe single scheduling interval; λ(t) is a grid electricity price at thetime t; c is a price of natural gas; F_(i) ^(c)(t) is a linear functionbetween a gas consumption and an electric output of the ith combinedheat and power equipment; and a_(i) and b_(i) are coefficients of thelinear function between the gas consumption and the electric output ofthe ith combined heat and power equipment;the target function of the real-time scheduling is as follows:

${\min \mspace{11mu} {J_{t}\left( {{X(t)},{A(t)}} \right)}} = {{c_{t}\left( {{X(t)},{A(t)}} \right)} + {E\;\left\lbrack {\sum\limits_{t_{1} = {t + 1}}^{t + T - 1}{c_{t_{1}}\left( {{X\left( t_{1} \right)},{A\left( t_{1} \right)}} \right)}} \right\rbrack}}$

in the formula, J_(t)(X(t),A(t)) is a function of the operating cost ofthe micro-grid system in the moving-horizon Markov decision cycle;

Furthermore, Step 3 specifically comprises the following sub-steps of:dividing a complete scheduling cycle into a plurality of schedulingintervals, finding a solution specific to a scheduling optimizationproblem in each of the scheduling intervals based on the greedyalgorithm respectively, and finally synthesizing locally optimalsolutions to respective scheduling intervals into one basic feasiblesolution across the complete scheduling interval.

Furthermore, the finding a solution specific to a schedulingoptimization problem in each of the scheduling intervals based on thegreedy algorithm respectively in Step 3 specifically comprises thefollowing sub-steps of:

-   (1) according to the operating cost function of the micro-grid    system in a single scheduling interval in Step 2, listing the target    function and the constraint conditions as follows:

${\min \mspace{11mu} {c_{t}\left( {{X(t)},{A(t)}} \right)}} = {{{{\lambda (t)} \cdot {p^{G}(t)} \cdot \Delta}\; T} + {\sum\limits_{i = 1}^{N}{c \cdot a_{i} \cdot {p_{i}^{c}(t)}}} + {\sum\limits_{i = 1}^{N}b_{i}}}$

the constraint conditions are as follows:

$\begin{matrix}\left\{ \begin{matrix}{{{p^{G}(t)} + {\sum\limits_{i = 1}^{N}{p_{i}^{c}(t)}} + {p^{B}(t)} + {p^{w}(t)}} = {p^{D}(t)}} \\{{\underset{\_}{pb}(t)} \leq {p^{B}(t)} \leq {\overset{\_}{pb}(t)}} \\{{\underset{\_}{pg}(t)} \leq {p^{G}(t)} \leq {\overset{\_}{pg}(t)}} \\{{{{\underset{\_}{{pc}_{i}}(t)} \leq {p_{i}^{c}(t)} \leq {{\overset{\_}{{pc}_{i}}(t)}\mspace{14mu} i}} = 1},2,\ldots \mspace{14mu},N}\end{matrix} \right. & \; \\{{Wherein}\mspace{689mu}} & \; \\\left\{ \begin{matrix}{{\underset{\_}{pb}(t)} = {\max \mspace{11mu} \left\{ {{\left( {{E(t)} - \overset{\_}{E}} \right)\text{/}\left( {\Delta \; {T \cdot \alpha_{c}}} \right)},{- \overset{\_}{p^{B}}}} \right\}}} \\{{\overset{\_}{pb}(t)} = {\min \mspace{11mu} \left\{ {{{\left( {{E(t)} - \underset{\_}{E}} \right) \cdot \alpha_{d}}\text{/}\Delta \; T},\overset{\_}{p^{B}}} \right\}}} \\{{\underset{\_}{pg}(t)} = {\max \mspace{11mu} \left\{ {\underset{\_}{p^{G}},{{p^{G}\left( {t - 1} \right)} - \delta}} \right\}}} \\{{\overset{\_}{pg}(t)} = {\min \mspace{11mu} \left\{ {\overset{\_}{p^{G}},{{p^{G}\left( {t - 1} \right)} + \delta}} \right\}}} \\{{\underset{\_}{{pc}_{i}}(t)} = {\max \mspace{11mu} \left\{ {0,{\underset{\_}{v_{i}} \cdot {H_{i}(t)}}} \right\}}} \\{{\overset{\_}{{pc}_{i}}(t)} = {\min \mspace{11mu} \left\{ {\overset{\_}{p_{i}^{c}},{\overset{\_}{v_{i}} \cdot {H_{i}(t)}}} \right\}}}\end{matrix} \right. & \;\end{matrix}$

in the formulae, pb(t) and pb(t) are new lower and upper limits of thecharging/discharging power of the storage battery during building of thebasic feasible solution respectively; pg(t) and pg(t) are new lower andupper limits of the exchange power between the micro-grid and main gridduring the building of the basic feasible solution respectively; pc_(i)(t) and pc_(i) (t) are new lower and upper limits of the electric outputof the ith combined heat and power equipment during the building of thebasic feasible solution respectively;

-   (2) to obtain an optimized result in one of the scheduling    intervals, ranking N+2 coefficients of the target function in an    ascending order to obtain a scheduling sequence for respective    decision variables, wherein the N+2 coefficients are λ(t)·ΔT, 0,    c·a_(i)(i=1, 2, . . . N), and the presence of the coefficient 0 is    because the target function does not comprise p^(B)(t); then forcing    a load difference to be d(t)=p^(D)(t)−p^(w)(t); and finding a final    optimized result according to the value of d(t) and the scheduling    sequence of respective decision variables; and-   (3) finding solutions for other scheduling intervals in turn to    obtain one basic feasible solution meeting the micro-grid operating    demand finally.

Furthermore, Step 4 specifically comprises the following sub-steps of:

-   (1) supposing the basic feasible solution obtained from Step 3 to be    π_(b)=(A_(b,t+1), A_(b,t+2), . . . , A_(b,t+T−1)) to obtain an    approximate value of the target function for the minimum operating    cost from the time t to the time t+T −1 during one real-time    scheduling:

${\overset{\_}{J_{t}}\left( {{X(t)},{A(t)}} \right)} = {{c_{t}\left( {{X(t)},{A(t)}} \right)} + {E\;\left\lbrack {\sum\limits_{t_{1} = {t + 1}}^{t + T - 1}{c_{t_{1}}\left( {{X\left( t_{1} \right)},A_{b,t_{1}}} \right)}} \right\rbrack}}$

in the formula, J_(t) (X(t), A(t)) is a function of the operating costof the micro-grid system in the moving-horizon Marcov decision cycle assolved with the Rollout algorithm

-   (2) forcing the approximate value to approach the minimum with the    Rollout algorithm:

$\overset{\_}{a_{t}} = {\arg \mspace{11mu} {\min\limits_{A{(t)}}\mspace{11mu} {\overset{\_}{J_{t}}\left( {{X(t)},{A(t)}} \right)}}}$

-   (3) according to X(t) in one of the current scheduling intervals and    in combination with a generated output value of wind power at the    time t, finding X(t+1) in a next scheduling interval, finding the    generated output value of the wind power at the time t+1 at the same    time, then calculating X(t+2) in the next scheduling interval, and    repeating the process until the whole scheduling cycle ends.

The present invention has the following advantages and positive effects:

-   1. The present invention provides the method for real-time    scheduling of multi-energy complementary micro-grids based on the    Rollout algorithm, where at first, the moving-horizon Markov    decision process model for multi-energy complementary micro-grid    real-time scheduling is set up, and the constraint conditions for    the real-time scheduling are established; then, a complete    scheduling cycle is divided into a plurality of scheduling    intervals, and one basic feasible solution meeting the constraint    conditions for the real-time scheduling is found based on the greedy    algorithm; and finally, a problem on the moving-horizon Markov    decision for the multi-energy complementary micro-grids is solved by    using the Rollout algorithm based on the basic feasible solution    above. With the setup of the moving-horizon model for the    multi-energy complementary micro-grids, the present invention finds    the solution by using the Rollout algorithm, which is simple and    effective with high practicability.-   2. The present invention finds the solution to the problem on the    moving-horizon Markov decision model by using the Rollout algorithm,    where a Markov decision model incorporating random new-energy    outputs is set up at first, one basic feasible solution is found    with the greedy algorithm, and the goal is approached based on this    with the consideration of the fluctuations in the new-energy output,    and the problems of low speed and efficiency of the traditional    algorithm are solved at the same time, enabling high-speed efficient    real-time scheduling for the multi-energy complementary micro-grids.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a process flow diagram of the present invention.

DETAILED DESCRIPTION

The embodiments of the present invention are further described in detailbelow with reference to the accompanying drawings:

The present invention provides a method for real-time scheduling ofmulti-energy complementary micro-grids based on the Rollout algorithm,which not only takes the fluctuations in the new energy outputs intoconsideration, but also more effectively solves the problem on themoving-horizon scheduling of the multi-energy complementary micro-grids,solving the problem on the moving-horizon Marcov decision model with theRollout algorithm. According to the method, at first, the moving-horizonMarkov decision process model for multi-energy complementary micro-gridreal-time scheduling with random new-energy output is set up, and theconstraint conditions and the target function for the real-timescheduling are established; then, a complete scheduling cycle is dividedinto a plurality of scheduling intervals, and one basic feasiblesolution meeting the constraint conditions for the real-time schedulingis found based on the greedy algorithm; and finally, a solution to themoving-horizon Markov decision model for multi-energy complementarymicro-grids is found by using the Rollout algorithm based on the basicfeasible solution above.

A method for real-time scheduling of multi-energy complementarymicro-grids based on a Rollout algorithm, as shown in FIG. 1, comprisesthe following steps of:

Step 1, setting up a moving-horizon Markov decision process model forthe real-time scheduling of the multi-energy complementary micro-gridswith random new-energy outputs, and establishing constraint conditionsfor the real-time scheduling;

where the constraint conditions established for the real-time schedulingin Step 1 comprises: micro-grid electric equilibrium constraints,storage battery operating constraints, exchange electric powerconstraints for the micro-grids and a main grid, and electric poweroutput constraints for combined heat and power equipment;the micro-grid electric equilibrium constraints are as follows:

${{p^{G}(t)} + {\sum\limits_{i = 1}^{N}{p_{i}^{c}(t)}} + {p^{B}(t)} + {p^{w}(t)}} = {p^{D}(t)}$

in the formula, t is a time parameter; p^(G)(t) is exchange electricpower for the micro-grids and the main grid at a time t, which ispositive during purchasing of electricity from the main grid andnegative during selling of electricity to the main grid; N is thequantity of the combined heat and power equipment; p_(i) ^(c)(t) isoutput electric power of the ith combined heat and power equipment atthe time t; p^(B)(1) is charging/discharging power of the storagebattery at the time t, which is negative during charging and positiveduring discharging; p^(w)(t) is generated output of wind power at thetime t; and p^(D)(t) is an electric load demand at the time t;the storage battery operating constraints are as follows:

$\quad\left\{ \begin{matrix}{{E\left( {t + 1} \right)} = {{E(t)} - {{{p^{B}(t)} \cdot \Delta}\; {T \cdot \alpha_{c}}}}} \\{{E\left( {t + 1} \right)} = {{E(t)} - {{{p^{B}(t)} \cdot \Delta}\; T\text{/}\alpha_{d}}}} \\{\underset{\_}{E} \leq {E(t)} \leq \overset{\_}{E}} \\{{{p^{B}(t)}} \leq \overset{\_}{p^{B}}}\end{matrix} \right.$

in the formulae, E(t) and E(t+1) are energy storage levels of thestorage battery at the time t and a time t+1 respectively; E and Ē areupper and lower boundaries of the capacity of the storage batteryrespectively; ΔT is a time interval from the time t to the time t+1;a_(c) and a_(d) are charging and discharging efficiencies of the storagebattery respectively; p^(B)(t) is charging/discharging power of thestorage battery at the time t, which is negative during charging andpositive during discharging; and p^(B) is an upper limit value of thecharging/discharging power of the storage battery;the exchange electric power constraints for the micro-grids and the maingrid are as follows:

$\quad\left\{ \begin{matrix}{{{p^{G}(t)}} \leq \overset{\_}{p^{G}}} \\{{{{p^{G}(t)} - {p^{G}\left( {t - 1} \right)}}} \leq \delta}\end{matrix} \right.$

in the formulae, p^(G)(t) and p^(G)(t−1) are exchange electric powerbetween the micro-grids and the main grid at the times t and t−1respectively, which is positive during purchasing of electricity fromthe main grid and negative during selling of electricity to the maingrid; p^(G) is an upper limit value of the exchange electric powerbetween the micro-grids and the main grid, and δ is an upper fluctuationlimit of the exchange electric power between the micro-grids and themain grid;the electric power output constraints for the combined heat and powerequipment are as follows:

$\quad\left\{ \begin{matrix}{{0 \leq {p_{i}^{c}(t)} \leq \overset{\_}{p_{i}^{c}}},{i = 1},2,\ldots \mspace{14mu},N} \\{{\underset{\_}{v_{i}} \cdot {H_{i}(t)}} \leq {p_{i}^{c}(t)} \leq {\overset{\_}{v_{i}} \cdot {H_{i}(t)}}}\end{matrix} \right.$

in the formulae, p_(i) ^(c)(t) is output electric power of the ithcombined heat and power equipment at the time t; p_(i) ^(c) is an upperlimit of the output electric power of the ith combined heat and powerequipment; H_(i)(t) is thermal power required to be supplied by the ithcombined heat and power equipment at the time t; v_(i) and v_(i) arelower and upper limits of electric-thermal power conversion efficiencyof the ith combined heat and power equipment.

Step 2, establishing a target function of the real-time scheduling forthe moving-horizon Markov decision process model for the real-timescheduling of the multi-energy complementary micro-grids with randomnew-energy outputs, with the goal of minimum operating cost of amicro-grid system in a moving-horizon Markov decision cycle;

wherein when in a grid-connected state with the main grid, themulti-energy complementary micro-grids can exchange electricity with themain grid, with energy supply equipment comprising wind drivengenerators, combined heat and power (CHP) equipment, and storagebatteries; and the target function for real-time scheduling is toachieve the minimum operating cost, including system electricitypurchasing cost and fuel cost of the CHP equipment, for the micro-gridsystem.the establishing a target function of the real-time scheduling for themoving-horizon Markov decision process model for the real-timescheduling of the multi-energy complementary micro-grids with randomnew-energy outputs specifically comprises the following sub-steps of:first setting up an operating cost function of the micro-grid system ata single scheduling interval with the goal of minimum operating cost ofthe micro-grid system at the single scheduling interval, and thenestablishing a target function of the real-time scheduling with the goalof the minimum operating cost of the micro-grid system in themoving-horizon Markov decision cycle;the operating cost function of the micro-grid system at the singlescheduling interval is as follows:

$\begin{matrix}{{c_{t}\left( {{X(t)},{A(t)}} \right)} = {{{{\lambda (t)} \cdot {p^{G}(t)} \cdot \Delta}\; T} + {c \cdot {\sum\limits_{i = 1}^{N}{F_{i}^{c}(t)}}}}} & \; \\{{Wherein}\mspace{689mu}} & \; \\\left\{ \begin{matrix}{{F_{i}^{c}(t)} = {{a_{i} \cdot {p_{i}^{c}(t)}} + b_{i}}} \\{{X(t)} = \left\lbrack {{E(t)},{p^{G}\left( {t - 1} \right)},{p^{w}(t)}} \right\rbrack} \\{{A(t)} = \left\lbrack {{p_{i}^{c}(t)},{p^{G}(t)},{p^{B}(t)}} \right\rbrack}\end{matrix} \right. & \;\end{matrix}$

in the formulae, X(t) is a state variable of the micro-grid system atthe time t; A(t) is a control variable of the micro-grid system at thetime t; c_(t)(X(t),A(t)) is a function of system operating cost at thesingle scheduling interval; λ(t) is a grid electricity price at the timet; c is a price of natural gas; F_(i) ^(c)(t) is a linear functionbetween a gas consumption and an electric output of the ith combinedheat and power equipment; and a_(i) and b_(i) are coefficients of thelinear function between the gas consumption and the electric output ofthe ith combined heat and power equipment;the target function of the real-time scheduling is as follows:

${\min \mspace{11mu} {J_{t}\left( {{X(t)},{A(t)}} \right)}} = {{c_{t}\left( {{X(t)},{A(t)}} \right)} + {E\;\left\lbrack {\sum\limits_{t_{1} = {t + 1}}^{t + T - 1}{c_{t_{1}}\left( {{X\left( t_{1} \right)},{A\left( t_{1} \right)}} \right)}} \right\rbrack}}$

in the formula, J_(t)(X(t),A(t)) is a function of the operating cost ofthe micro-grid system in the moving-horizon Markov decision cycle;

Step 3, dividing a single complete scheduling cycle into a plurality ofscheduling intervals, and finding one basic feasible solution meetingthe constraint conditions for the real-time scheduling based on a greedyalgorithm;

where Step 3 specifically comprises the following sub-steps of: dividinga complete scheduling cycle into a plurality of scheduling intervals,finding a solution specific to a scheduling optimization problem in eachof the scheduling intervals based on the greedy algorithm respectively,and finally synthesizing locally optimal solutions to respectivescheduling intervals into one basic feasible solution across thecomplete scheduling interval.the finding a solution specific to a scheduling optimization problem ineach of the scheduling intervals based on the greedy algorithmrespectively in Step 3 specifically comprises the following sub-stepsof:

-   (1) according to the operating cost function of the micro-grid    system in a single scheduling interval in Step 2, listing the target    function and the constraint conditions as follows:

${\min \mspace{11mu} {c_{t}\left( {{X(t)},{A(t)}} \right)}} = {{{{\lambda (t)} \cdot {p^{G}(t)} \cdot \Delta}\; T} + {\sum\limits_{i = 1}^{N}{c \cdot a_{i} \cdot {p_{i}^{c}(t)}}} + {\sum\limits_{i = 1}^{N}b_{i}}}$

the constraint conditions are as follows:

$\begin{matrix}\left\{ \begin{matrix}{{{p^{G}(t)} + {\sum\limits_{i = 1}^{N}{p_{i}^{c}(t)}} + {p^{B}(t)} + {p^{w}(t)}} = {p^{D}(t)}} \\{{\underset{\_}{pb}(t)} \leq {p^{B}(t)} \leq {\overset{\_}{pb}(t)}} \\{{\underset{\_}{pg}(t)} \leq {p^{G}(t)} \leq {\overset{\_}{pg}(t)}} \\{{{{\underset{\_}{{pc}_{i}}(t)} \leq {p_{i}^{c}(t)} \leq {{{pc}_{i}(t)}\mspace{14mu} i}} = 1},2,\ldots \mspace{14mu},N}\end{matrix} \right. & \; \\{{wherein}\mspace{695mu}} & \; \\\left\{ \begin{matrix}{{\underset{\_}{pb}(t)} = {\max \mspace{11mu} \left\{ {{\left( {{E(t)} - \overset{\_}{E}} \right)\text{/}\left( {\Delta \; {T \cdot \alpha_{c}}} \right)},{- \overset{\_}{p^{B}}}} \right\}}} \\{{\overset{\_}{pb}(t)} = {\min \mspace{11mu} \left\{ {{{\left( {{E(t)} - \underset{\_}{E}} \right) \cdot \alpha_{d}}\text{/}\Delta \; T},\overset{\_}{p^{B}}} \right\}}} \\{{\underset{\_}{pg}(t)} = {\max \mspace{11mu} \left\{ {\underset{\_}{p^{G}},{{p^{T}\left( {t - 1} \right)} - \delta}} \right\}}} \\{{\overset{\_}{pg}(t)} = {\min \mspace{11mu} \left\{ {\overset{\_}{p^{G}},{{p^{G}\left( {t - 1} \right)} + \delta}} \right\}}} \\{{\underset{\_}{{pc}_{i}}(t)} = {\max \mspace{11mu} \left\{ {0,{\underset{\_}{v_{i}} \cdot {H_{i}(t)}}} \right\}}} \\{{\overset{\_}{{pc}_{i}}(t)} = {\min \mspace{11mu} \left\{ {\overset{\_}{p_{i}^{c}},{\overset{\_}{v_{i}} \cdot {H_{i}(t)}}} \right\}}}\end{matrix} \right. & \;\end{matrix}$

in the formulae, pb (t)and pb(t) are new lower and upper limits of thecharging/discharging power of the storage battery during building of thebasic feasible solution respectively; pg(t) and pg(t) are new lower andupper limits of the exchange power between the micro-grid and main gridduring the building of the basic feasible solution respectively; pc_(i)(t) and pc_(i) (t) are new lower and upper limits of the electric outputof the ith combined heat and power equipment during the building of thebasic feasible solution respectively;

-   (2) to obtain an optimized result in one of the scheduling    intervals, ranking N+2 coefficients of the target function in an    ascending order to obtain a scheduling sequence for respective    decision variables, wherein the N+2 coefficients are λ(t)·ΔT, 0,    c·a_(i) (i=1, 2, . . . , N), the presence of the coefficient 0 is    because the target function does not comprise p^(B) (t); then    forcing a load difference to be d(t)=p^(D)(t)−p^(w)(t); and finding    a final optimized result according to the value of d(t) and the    scheduling sequence of respective decision variables; and-   (3) finding solutions for other scheduling intervals in turn to    obtain one basic feasible solution meeting the micro-grid operating    demand finally.

Step 4, finding a solution to the moving-horizon Markov decision processmodel for the real-time scheduling of the multi-energy complementarymicro-grids by using the Rollout algorithm based on the basic feasiblesolution from Step 3.

Step 4 specifically comprises the following sub-steps of:

-   (1) supposing the basic feasible solution obtained from Step 3 to be    π_(b)=(A_(b,t+1), A_(b,t+2), . . . , A_(b,t+T−1)) to obtain an    approximate value of the target function for the minimum operating    cost from the time t to the time t+T−1 during one real-time    scheduling:

${\overset{\_}{J_{t}}\left( {{X(t)},{A(t)}} \right)} = {{c_{t}\left( {{X(t)},{A(t)}} \right)} + {E\;\left\lbrack {\sum\limits_{t_{1} = {t + 1}}^{t + T - 1}{c_{t_{1}}\left( {{X\left( t_{1} \right)},A_{b,t_{1}}} \right)}} \right\rbrack}}$

in the formula, J_(t) (X(t),A(0) is a function of the operating cost ofthe micro-grid system in the moving-horizon Marcov decision cycle assolved with the Rollout algorithm

-   (2) forcing the approximate value to approach the minimum with the    Rollout algorithm:

$\overset{\_}{a_{t}} = {\arg \mspace{11mu} {\min\limits_{A{(t)}}{\overset{\_}{J_{t}}\left( {{X(t)},{A(t)}} \right)}}}$

-   (3) according to X(t) in one of the current scheduling intervals and    in combination with a generated output value of wind power at the    time t, finding X (t+1) in a next scheduling interval, finding the    generated output value of the wind power at the time t+1 at the same    time, then calculating X(t+2) in the next scheduling interval, and    repeating the process until the whole scheduling cycle ends.

It should be noted that the described embodiments of the presentinvention are for an illustrative purpose rather than a limitingpurpose, and the present invention thus includes but not limited to theembodiments described in the Description of Preferred Embodiments. Anyother embodiments obtained by those skilled in the art according to thetechnical solution of the present invention likewise fall within theprotection scope of the present invention.

1. A method for real-time scheduling of multi-energy complementarymicro-grids based on a Rollout algorithm, characterized by comprisingthe following steps of: Step 1, setting up a moving-horizon Markovdecision process model for the real-time scheduling of the multi-energycomplementary micro-grids with random new-energy outputs, andestablishing constraint conditions for the real-time scheduling; Step 2,establishing a target function of the real-time scheduling for themoving-horizon Markov decision process model for the real-timescheduling of the multi-energy complementary micro-grids with the randomnew-energy outputs, with the goal of minimum operating cost of amicro-grid system in a moving-horizon Markov decision cycle; Step 3,dividing a single complete scheduling cycle into a plurality ofscheduling intervals, and finding one basic feasible solution meetingthe constraint conditions for the real-time scheduling based on a greedyalgorithm; and Step 4, finding a solution to the moving-horizon Markovdecision process model for the real-time scheduling of the multi-energycomplementary micro-grids by using the Rollout algorithm based on thebasic feasible solution from Step
 3. 2. The method for real-timescheduling of multi-energy complementary micro-grids based on theRollout algorithm according to claim 1, characterized in that theconstraint conditions established for the real-time scheduling in Step 1comprises: micro-grid electric equilibrium constraints, storage batteryoperating constraints, exchange electric power constraints for themicro-grids and a main grid, and electric power output constraints forcombined heat and power equipment; the micro-grid electric equilibriumconstraints are as follows:${{p^{G}(t)} + {\sum\limits_{i = 1}^{N}{p_{i}^{c}(t)}} + {p^{B}(t)} + {p^{w}(t)}} = {p^{D}(t)}$in the formula, t is a time parameter; p^(G) (t) is exchange electricpower for the micro-grids and the main grid at a time t, which ispositive during purchasing of electricity from the main grid andnegative during selling of electricity to the main grid; N is thequantity of the combined heat and power equipment; p_(i) ^(c)(t) isoutput electric power of the ith combined heat and power equipment atthe time t; p^(B) (t) is charging/discharging power of the storagebattery at the time t, which is negative during charging and positiveduring discharging; p^(w)(t) is generated output of wind power at thetime t; and p^(D) (t) is an electric load demand at the time t; thestorage battery operating constraints are as follows:$\quad\left\{ \begin{matrix}{{E\left( {t + 1} \right)} = {{E(t)} - {{{p^{B}(t)} \cdot \Delta}\; {T \cdot \alpha_{c}}}}} \\{{E\left( {t + 1} \right)} = {{E(t)} - {{{p^{B}(t)} \cdot \Delta}\; T\text{/}\alpha_{d}}}} \\{\underset{\_}{E} \leq {E(t)} \leq \overset{\_}{E}} \\{{{p^{B}(t)}} \leq \overset{\_}{p^{B}}}\end{matrix} \right.$ in the formulae, E(t) and E(t+1) are energystorage levels of the storage battery at the time t and a time t+1respectively; E and Ē are upper and lower boundaries of the capacity ofthe storage battery respectively; ΔT is a time interval from the time tto the time t+1; a_(c), and a_(d) are charging and dischargingefficiencies of the storage battery respectively; p^(B)(t) ischarging/discharging power of the storage battery at the time t, whichis negative during charging and positive during discharging; and p^(B)is an upper limit value of the charging/discharging power of the storagebattery; the exchange electric power constraints for the micro-grids andthe main grid are as follows: $\quad\left\{ \begin{matrix}{{{p^{G}(t)}}{\quad{\quad{\leq \overset{\_}{p^{G}}}}}} \\{{{{p^{G}(t)} - {p^{G}\left( {t - 1} \right)}}} \leq \delta}\end{matrix} \right.$ in the formulae, p^(G)(_(t)) and p^(G)(t−1) areexchange electric power between the micro-grids and the main grid at thetimes t and t−1 respectively, which is positive during purchasing ofelectricity from the main grid and negative during selling ofelectricity to the main grid; p^(G) is an upper limit value of theexchange electric power between the micro-grids and the main grid, and δis an upper fluctuation limit of the exchange electric power between themicro-grids and the main grid; the electric power output constraints forthe combined heat and power equipment are as follows:$\quad\left\{ \begin{matrix}{0 \leq {{p_{i}^{c}(t)}{\quad{{\leq \overset{\_}{p_{i}^{c}}},\mspace{14mu} {i = 1},2,\ldots \mspace{14mu},N}}}} \\{{\underset{\_}{v_{i}} \cdot {H_{i}(t)}} \leq {p_{i}^{c}(t)} \leq {\overset{\_}{v_{i}} \cdot {H_{i}(t)}}}\end{matrix} \right.$ in the formulae, p_(i) ^(c)(t) is output electricpower of the ith combined heat and power equipment at the time t; p_(i)^(c) is an upper limit of the output electric power of the ith combinedheat and power equipment; H_(i)(t) is thermal power required to besupplied by the ith combined heat and power equipment at the time t;v_(i) and v_(i) are lower and upper limits of electric-thermal powerconversion efficiency of the ith combined heat and power equipment. 3.The method for real-time scheduling of multi-energy complementarymicro-grids based on the Rollout algorithm according to claim 1, whereinthe establishing a target function of the real-time scheduling for themoving-horizon Markov decision process model for the real-timescheduling of the multi-energy complementary micro-grids with randomnew-energy outputs in Step 2 specifically comprises the followingsub-steps of: first setting up an operating cost function of themicro-grid system at a single scheduling interval with the goal ofminimum operating cost of the micro-grid system at the single schedulinginterval, and then establishing a target function of the real-timescheduling with the goal of the minimum operating cost of the micro-gridsystem in the moving-horizon Markov decision cycle; the operating costfunction of the micro-grid system at the single scheduling interval isas follows: $\begin{matrix}{{c_{t}\left( {{X(t)},{A(t)}} \right)} = {{{{\lambda (t)} \cdot {p^{G}(t)} \cdot \Delta}\; T} + {c \cdot {\sum\limits_{i = 1}^{N}{F_{i}^{c}(t)}}}}} & \; \\{{Wherein}\mspace{689mu}} & \; \\\left\{ \begin{matrix}{{F_{i}^{c}(t)} = {{a_{i} \cdot {p_{i}^{c}(t)}} + b_{i}}} \\{{X(t)} = \left\lbrack {{E(t)},{p^{G}\left( {t - 1} \right)},{p^{w}(t)}} \right\rbrack} \\{{A(t)} = \left\lbrack {{p_{i}^{c}(t)},{p^{G}(t)},{p^{B}(t)}} \right\rbrack}\end{matrix} \right. & \;\end{matrix}$ in the formulae, X(t) is a state variable of themicro-grid system at the time t; A(t) is a control variable of themicro-grid system at the time t; c_(t)(X(t), A(t)) is a function ofsystem operating cost at the single scheduling interval; λ(t) is a gridelectricity price at the time t; c is a price of natural gas; F_(i)^(c)(t) is a linear function between a gas consumption and an electricoutput of the ith combined heat and power equipment; and a_(i) and b_(i)are coefficients of the linear function between the gas consumption andthe electric output of the ith combined heat and power equipment; thetarget function of the real-time scheduling is as follows:${\min \mspace{11mu} {J_{t}\left( {{X(t)},{A(t)}} \right)}} = {{c_{t}\left( {{X(t)},{A(t)}} \right)} + {E\;\left\lbrack {\sum\limits_{t_{1} = {t + 1}}^{t + T - 1}{c_{t_{1}}\left( {{X\left( t_{1} \right)},{A\left( t_{1} \right)}} \right)}} \right\rbrack}}$in the formula, J_(t)(X (t), A(t)) is a function of the operating costof the micro-grid system in the moving-horizon Markov decision cycle. 4.The method for real-time scheduling of multi-energy complementarymicro-grids based on the Rollout algorithm according to claim 1, whereinStep 3 specifically comprises the following sub-steps of: dividing acomplete scheduling cycle into a plurality of scheduling intervals,finding a solution specific to a scheduling optimization problem in eachof the scheduling intervals based on the greedy algorithm respectively,and finally synthesizing locally optimal solutions to respectivescheduling intervals into one basic feasible solution across thecomplete scheduling interval.
 5. The method for real-time scheduling ofmulti-energy complementary micro-grids based on the Rollout algorithmaccording to claim 4, characterized in that the finding a solutionspecific to a scheduling optimization problem in each of the schedulingintervals based on the greedy algorithm respectively in Step 3specifically comprises the following sub-steps of: (1) according to theoperating cost function of the micro-grid system in a single schedulinginterval in Step 2, listing the target function and the constraintconditions as follows:${\min \mspace{11mu} {c_{t}\left( {{X(t)},{A(t)}} \right)}} = {{{{\lambda (t)} \cdot {p^{G}(t)} \cdot \Delta}\; T} + {\sum\limits_{i = 1}^{N}{c \cdot a_{i} \cdot {p_{i}^{c}(t)}}} + {\sum\limits_{i = 1}^{N}b_{i}}}$the constraint conditions are as follows: $\begin{matrix}\left\{ \begin{matrix}{{{p^{G}(t)} + {\sum\limits_{i = 1}^{N}{p_{i}^{c}(t)}} + {p^{B}(t)} + {p^{w}(t)}} = {p^{D}(t)}} \\{{\underset{\_}{pb}(t)} \leq {p^{B}(t)} \leq {\overset{\_}{pb}(t)}} \\{{\underset{\_}{pg}(t)} \leq {p^{G}(t)} \leq {\overset{\_}{pg}(t)}} \\{{{{\underset{\_}{{pc}_{i}}(t)} \leq {p_{i}^{c}(t)} \leq {{\overset{\_}{{pc}_{i}}(t)}\mspace{14mu} i}} = 1},2,\ldots \mspace{14mu},N}\end{matrix} \right. & \; \\{{wherein}\mspace{695mu}} & \; \\\left\{ \begin{matrix}{{\underset{\_}{pb}(t)} = {\max \mspace{11mu} \left\{ {{\left( {{E(t)} - \overset{\_}{E}} \right)\text{/}\left( {\Delta \; {T \cdot \alpha_{c}}} \right)},{- \overset{\_}{p^{B}}}} \right\}}} \\{{\overset{\_}{pb}(t)} = {\min \mspace{11mu} \left\{ {{{\left( {{E(t)} - \underset{\_}{E}} \right) \cdot \alpha_{d}}\text{/}\Delta \; T},\overset{\_}{p^{B}}} \right\}}} \\{{\underset{\_}{pg}(t)} = {\max \mspace{11mu} \left\{ {\underset{\_}{p^{G}},{{p^{G}\left( {t - 1} \right)} - \delta}} \right\}}} \\{{\overset{\_}{pg}(t)} = {\min \mspace{11mu} \left\{ {\overset{\_}{p^{G}},{{p^{G}\left( {t - 1} \right)} + \delta}} \right\}}} \\{{\underset{\_}{{pc}_{i}}(t)} = {\max \mspace{11mu} \left\{ {0,{\underset{\_}{v_{i}} \cdot {H_{i}(t)}}} \right\}}} \\{{\overset{\_}{{pc}_{i}}(t)} = {\min \mspace{11mu} \left\{ {\overset{\_}{p_{i}^{c}},{\overset{\_}{v_{i}} \cdot {H_{i}(t)}}} \right\}}}\end{matrix} \right. & \;\end{matrix}$ in the formulae, pb(t) and pb(t) are new lower and upperlimits of the charging/discharging power of the storage battery duringbuilding of the basic feasible solution respectively; pg(t) and pg(t)are new lower and upper limits of the exchange power between themicro-grid and main grid during the building of the basic feasiblesolution respectively; pc_(i) (t) and pc_(i) (t) are new lower and upperlimits of the electric output of the ith combined heat and powerequipment during the building of the basic feasible solutionrespectively; (2) to obtain an optimized result in one of the schedulingintervals, ranking N+2 coefficients of the target function in anascending order to obtain a scheduling sequence for respective decisionvariables, wherein the N+2 coefficients are λ(t)·ΔT, 0, c·a_(i)(i=1, 2,. . . , N), and the presence of the coefficient 0 is because the targetfunction does not comprise p^(B)(t); then forcing a load difference tobe d(t)=p^(D)(t)−p^(w)(t); and finding a final optimized resultaccording to the value of d(t) and the scheduling sequence of respectivedecision variables; and (3) finding solutions for other schedulingintervals in turn to obtain one basic feasible solution meeting themicro-grid operating demand finally.
 6. The method for real-timescheduling of multi-energy complementary micro-grids based on theRollout algorithm according to claim 1, wherein Step 4 specificallycomprises the following sub-steps of: (1) supposing the basic feasiblesolution obtained from Step 3 to be π_(b)=(A_(b,t+1), A_(b,t+2), . . . ,A_(b,t+T−1)) to obtain an approximate value of the target function forthe minimum operating cost from the time t to the time t+T−1 during onereal-time scheduling:${\overset{\_}{J_{t}}\left( {{X(t)},{A(t)}} \right)} = {{c_{t}\left( {{X(t)},{A(t)}} \right)} + {E\;\left\lbrack {\sum\limits_{t_{1} = {t + 1}}^{t + T - 1}{c_{t_{1}}\left( {{X\left( t_{1} \right)},A_{b,t_{1}}} \right)}} \right\rbrack}}$in the formula, J_(t) (X(t), A(t)) is a function of the operating costof the micro-grid system in the moving-horizon Marcov decision cycle assolved with the Rollout algorithm; (2) forcing the approximate value toapproach the minimum with the Rollout algorithm:$\overset{\_}{a_{t}} = {\arg \mspace{11mu} {\min\limits_{A{(t)}}{\overset{\_}{J_{t}}\left( {{X(t)},{A(t)}} \right)}}}$(3) according to X(t) in one of the current scheduling intervals and incombination with a generated output value of wind power at the time t,finding X(t+1) in a next scheduling interval, finding the generatedoutput value of the wind power at the time t+1 at the same time, thencalculating X(t+2) in the next scheduling interval, and repeating theprocess until the whole scheduling cycle ends.